Bayesian updating for one-dimensional consolidation measurements

Kelly, Richard; Huang, Jinsong

doi:10.1139/cgj-2014-0338

Cited by 63 publications

(13 citation statements)

References 21 publications

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“…Bayes Theorem provides a theoretical framework to allow updating the predictions with the monitored data. This could be done in real-time allowing engineers to take advantage of updated predictions (e.g., Kelly and Huang 2015;. Quantitative probability of event occurrence could be continuously updated.…”

Section: Bayesian Updating and Observational Methodsmentioning

confidence: 99%

Quantitative Risk Assessment of Individual Landslides

Huang¹,

Griffiths²,

Fenton³

2019

Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

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Slope failures or landslides are reported every year in different countries. In spite of improvements in landslide hazard recognition, prediction, mitigation measures, and warning systems, worldwide landslide activity is increasing. Uncertainty is a dominant feature of all landslides. Various uncertainties arise during the resolution of the problem, from climate data of rainfall, to infiltration rate, to site characterization, to material properties, to analysis, design and consequence assessment. These variabilities are rarely taken into account directly in traditional geotechnical analysis, rather some "average" or suitably "pessimistic" property is assumed to act across the whole region of interest. This keynote will focus on the modelling of spatial variability of soil properties in the quantitative risk assessment of individual landslides. Firstly, infinite (1D), two dimensional (2D) and three dimensional (3D) slope examples are used to demonstrate the importance of modelling spatial variability in the risk assessment of landslides. The infinite slope example shows that ignoring spatial variability will lead to unconservative estimation of slope failure. The 2D slope example shows that the slope failure can have multiple possible failure modes. The 3D slope example shows that ignoring the spatial variability in the third direction can lead to unconservative estimation of slope failure probability if the slope is long. Then the framework of quantitative risk assessment of landslides is discussed. Slope or landslides can fail shallowly or deeply. Because deep failure leads to more severe consequence than shallow failure, each potential failure mode has a specific consequence associated with it. It is thus necessary to redefine risk as mean consequence rather than assuming a constant consequence for all failure modes. One challenge for quantitative risk assessment of landslides is to automatically identify failure modes and assess associated consequences. This issue can be overcome by introducing Machine Learning algorithms within the framework of Monte Carlo simulations.

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Section: Bayesian Updating and Observational Methodsmentioning

confidence: 99%

Quantitative Risk Assessment of Individual Landslides

Huang¹,

Griffiths²,

Fenton³

2019

Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

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“…These posterior distributions are obtained using the MCMC simulation, which is an effective random sampling method. The MCMC simulation can maintain adequate sampling density as the number of parameter increases and compute efficiently which has gained popularity in recent years to sample the posterior probability density function [30]. It can handle efficiently problems with a large number of random variables and is very flexible to any type of prior distribution.…”

Section: Markov Chain Monte Carlo Methodsmentioning

confidence: 99%

Probabilistic Analysis of Tunnel Face Stability below River Using Bayesian Framework

Liu

Luo

Huang

et al. 2018

Mathematical Problems in Engineering

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A key issue in assessment on tunnel face stability is a reliable evaluation of required support pressure on the tunnel face and its variations during tunnel excavation. In this paper, a Bayesian framework involving Markov Chain Monte Carlo (MCMC) simulation is implemented to estimate the uncertainties of limit support pressure. The probabilistic analysis for the three-dimensional face stability of tunnel below river is presented. The friction angle and cohesion are considered as random variables. The uncertainties of friction angle and cohesion and their effects on tunnel face stability prediction are evaluated using the Bayesian method. The three-dimensional model of tunnel face stability below river is based on the limit equilibrium theory and is adopted for the probabilistic analysis. The results show that the posterior uncertainty bounds of friction angle and cohesion are much narrower than the prior ones, implying that the reduction of uncertainty in cohesion and friction significantly reduces the uncertainty of limit support pressure. The uncertainty encompassed in strength parameters are greatly reduced by the MCMC simulation. By conducting uncertainty analysis, MCMC simulation exhibits powerful capability for improving the reliability and accuracy of computational time and calculations.

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“…Therefore, the confidence interval of the SWCC had a great influence on the seepage analysis of the slope under rainfall. Figure 10 depicts the pore-water pressure at point M (25,15) in the slope as shown in Figure 3 versus the rainfall duration time for the different PCT of the SWCC. The suction showed a gradual reduction during rainfall and indicated that the negative pore-water pressure at point M with the same rainfall duration decreased with an increase in the PCT value.…”

Section: Pore-water Pressure Profiles With Different Percentilesmentioning

confidence: 99%

“…The Markov chain Monte Carlo (MCMC) method [21] can effectively solve the posterior distribution, and it has greatly promoted the application of the Bayesian approach. At present, the MCMC method has been applied to analyzing the uncertainties in different research fields, such as flood frequency analysis [22], the ultimate capacity of piles [23], shear strength [24], and the consolidation coefficient [25]. Estimation of the parameters (including SWCC model parameters) under certain confidence levels can also be carried out by the MCMC method.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Liu

Luo

Huang

et al. 2017

Water

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Abstract:It is important to determine the soil-water characteristic curve (SWCC) for analyzing slope seepage and stability under the conditions of rainfall. However, SWCCs exhibit high uncertainty because of complex influencing factors, which has not been previously considered in slope seepage and stability analysis under conditions of rainfall. This study aimed to evaluate the uncertainty of the SWCC and its effects on the seepage and stability analysis of an unsaturated soil slope under conditions of rainfall. The SWCC model parameters were treated as random variables. An uncertainty evaluation of the parameters was conducted based on the Bayesian approach and the Markov chain Monte Carlo (MCMC) method. Observed data from granite residual soil were used to test the uncertainty of the SWCC. Then, different confidence intervals for the model parameters of the SWCC were constructed. The slope seepage and stability analysis under conditions of rainfall with the SWCC of different confidence intervals was investigated using finite element software (SEEP/W and SLOPE/W). The results demonstrated that SWCC uncertainty had significant effects on slope seepage and stability. In general, the larger the percentile value, the greater the reduction of negative pore-water pressure in the soil layer and the lower the safety factor of the slope. Uncertainties in the model parameters of the SWCC can lead to obvious errors in predicted pore-water pressure profiles and the estimated safety factor of the slope under conditions of rainfall.

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Bayesian updating for one-dimensional consolidation measurements

Cited by 63 publications

References 21 publications

Quantitative Risk Assessment of Individual Landslides

Quantitative Risk Assessment of Individual Landslides

Probabilistic Analysis of Tunnel Face Stability below River Using Bayesian Framework

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

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